Distributed Processing
This mode assumes that the data set is split into nblocks blocks across computation nodes.
Algorithm Parameters
The correlation and variance-covariance matrices algorithm in the distributed processing mode has the following parameters:
Algorithm Parameters for Correlation and Variance-Covariance Matrices Algorithm (Distributed Processing)
Parameter |
Default Valude |
Description |
|---|
computeStep
|
Not applicable |
The parameter required to initialize the algorithm. Can be:
step1Local - the first step, performed on local nodes
step2Master - the second step, performed on a master node
|
algorithmFPType
|
float
|
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
method
|
defaultDense
|
Available methods for computation of low order moments:
- defaultDense
default performance-oriented method
- singlePassDense
implementation of the single-pass algorithm proposed by D.H.D. West
- sumDense
implementation of the algorithm in the cases where the basic statistics associated with
the numeric table are pre-computed sums; returns an error if pre-computed sums are not defined
- fastCSR
performance-oriented method for CSR numeric tables
- singlePassCSR
implementation of the single-pass algorithm proposed by D.H.D. West; optimized for CSR numeric tables
- sumCSR
implementation of the algorithm in the cases where the basic statistics associated with
the numeric table are pre-computed sums; optimized for CSR numeric tables;
returns an error if pre-computed sums are not defined
|
outputMatrixType
|
covarianceMatrix
|
The type of the output matrix. Can be:
|
Computation of correlation and variance-covariance matrices follows the general schema described in Algorithms:
Step 1 - on Local Nodes
In this step, the correlation and variance-covariance matrices algorithm accepts the input described below.
Pass the Input ID as a parameter to the methods that provide input for your algorithm.
For more details, see Algorithms.
Step 1: Algorithm Input for Correlation and Variance-Covariance Matrices Algorithm (Distributed Processing)
Input ID |
Input |
|---|
data
|
Pointer to the numeric table of size \(n_i \times p\) that represents the \(i\)-th data block on the local node.
While the input for defaultDense, singlePassDense, or sumDense method can be an object of any class derived
from NumericTable, the input for fastCSR, singlePassCSR, or sumCSR method can only be an object of
the CSRNumericTable class.
|
In this step, the correlation and variance-covariance matrices algorithm calculates the results described below.
Pass the Result ID as a parameter to the methods that access the results of your algorithm.
For more details, see Algorithms.
Step 1: Algorithm Output for Correlation and Variance-Covariance Matrices Algorithm (Distributed Processing)
Result ID |
Result |
|---|
nObservations
|
Pointer to the \(1 \times 1\) numeric table that contains the number of observations processed so far on the local node.
Note
By default, this result is an object of the HomogenNumericTable class,
but you can define the result as an object of any class derived from NumericTable
except CSRNumericTable.
|
crossProduct
|
Pointer to \(p \times p\) numeric table with the cross-product matrix computed so far on the local node.
Note
By default, this table is an object of the HomogenNumericTable class,
but you can define the result as an object of any class derived from NumericTable
except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.
|
sum
|
Pointer to \(1 \times p\) numeric table with partial sums computed so far on the local node.
Note
By default, this table is an object of the HomogenNumericTable class,
but you can define the result as an object of any class derived from NumericTable
except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.
|
Step 2 - on Master Node
In this step, the correlation and variance-covariance matrices algorithm accepts the input described below.
Pass the Input ID as a parameter to the methods that provide input for your algorithm.
For more details, see Algorithms.
Step 2: Algorithm Input for Correlation and Variance-Covariance Matrices Algorithm (Distributed Processing)
Input ID |
Input |
|---|
partialResults
|
A collection that contains results computed in Step 1 on local nodes (nObservations, crossProduct, and sum).
Note
The collection can contain objects of any class derived from the NumericTable class
except PackedSymmetricMatrix and PackedTriangularMatrix.
|
In this step, the correlation and variance-covariance matrices algorithm calculates the results described in the following table.
Pass the Result ID as a parameter to the methods that access the results of your algorithm.
For more details, see Algorithms.
Step 2: Algorithm Output for for Correlation and Variance-Covariance Matrices Algorithm (Distributed Processing)
Result ID |
Result |
|---|
covariance
|
Use when outputMatrixType``=``covarianceMatrix. Pointer to the numeric table with the \(p \times p\) variance-covariance matrix.
Note
By default, this result is an object of the HomogenNumericTable class,
but you can define the result as an object of any class derived from NumericTable
except PackedTriangularMatrix and CSRNumericTable.
|
correlation
|
Use when outputMatrixType``=``correlationMatrix. Pointer to the numeric table with the \(p \times p\) correlation matrix.
Note
By default, this result is an object of the HomogenNumericTable class,
but you can define the result as an object of any class derived from NumericTable
except PackedTriangularMatrix and CSRNumericTable.
|
mean
|
Pointer to the \(1 \times p\) numeric table with means.
Note
By default, this result is an object of the HomogenNumericTable class,
but you can define the result as an object of any class derived from NumericTable
except PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.
|
Product and Performance Information |
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Performance varies by use, configuration and other factors.
Learn more at www.Intel.com/PerformanceIndex.
Notice revision #20201201
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